Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing

Abstract

To reduce the discrepancy between the results of the expected data and the actual data, prediction is a procedure that is calculated systematically based on owned historical and present information. For the creation of solar energy projects and for decision-making in other connected domains, solar radiation intensity prediction is essential. This study aims to create a predictive model on monthly global horizontal irradiance data. The method used is the Simple Moving Average algorithm, Exponentially Weighted Moving Average and Single Exponential Smoothing. The stages carried out in this study include data collection, data preprocessing, testing of predictive models, interpretation of data visualization, and performance evaluation. The results of calculating the error value and correlation produce an evaluation of the performance of the prediction model. The SES method, which obtained an MAE value of 7.13, a MAPE of 0.02%, an MSE of 88.07, an RMSE of 9.38, and an R² of 0.94, was determined to be the best prediction model by the calculation of the prediction model performance evaluation. A MAE value of 9.45, a MAPE of 0.02%, an MSE of 150.16, an RMSE of 12.25, and an R² of 0.91 were obtained by the EWMA method, which is also the method that produced the second-best result. A MAE value of 14.38, a MAPE of 0.04%, an MSE of 367.59, an RMSE of 19.17, and an R² of 0.77 were obtained by the SMA method, which is the third-best result.